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Leslie N. Smith
Leslie N. Smith
Verified email at nrl.navy.mil
Title
Cited by
Cited by
Year
Cyclical learning rates for training neural networks
LN Smith
2017 IEEE winter conference on applications of computer vision (WACV), 464-472, 2017
31022017
Super-convergence: Very fast training of neural networks using large learning rates
LN Smith, N Topin
Artificial intelligence and machine learning for multi-domain operations …, 2019
13982019
A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay
LN Smith
arXiv preprint arXiv:1803.09820, 2018
12112018
Improving dictionary learning: Multiple dictionary updates and coefficient reuse
LN Smith, M Elad
IEEE Signal Processing Letters 20 (1), 79-82, 2012
1592012
A disciplined approach to neural network hyper-parameters: Part 1—Learning rate, batch size, momentum, and weight decay. arXiv 2018
LN Smith
arXiv preprint arXiv:1803.09820, 1803
1371803
Rotational compound state resonances for an argon and methane scattering system
LN Smith, DJ Malik, D Secrest
The Journal of Chemical Physics 71 (11), 4502-4514, 1979
1051979
Deep convolutional neural network design patterns
LN Smith, N Topin
arXiv preprint arXiv:1611.00847, 2016
812016
Close‐coupling and coupled state calculations of argon scattering from normal methane
LN Smith, D Secrest
The Journal of Chemical Physics 74 (7), 3882-3897, 1981
591981
An approach to explainable deep learning using fuzzy inference
D Bonanno, K Nock, L Smith, P Elmore, F Petry
Next-Generation Analyst V 10207, 132-136, 2017
452017
Gradual dropin of layers to train very deep neural networks
LN Smith, EM Hand, T Doster
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
332016
Restoration of turbulence degraded underwater images
AV Kanaev, W Hou, S Woods, LN Smith
Optical Engineering 51 (5), 057007-057007, 2012
322012
A Disciplined Approach to Neural Network Hyper-Parameters: Part 1–Learning Rate
LN Smith
Batch size, Momentum, and Weight decay 8, 1803, 2018
302018
Disambiguation protocols based on risk simulation
DE Fishkind, CE Priebe, KE Giles, LN Smith, V Aksakalli
IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2007
292007
Exploring loss function topology with cyclical learning rates
LN Smith, N Topin
arXiv preprint arXiv:1702.04283, 2017
272017
Selecting subgoals using deep learning in minecraft: A preliminary report
D Bonanno, M Roberts, L Smith, DW Aha
IJCAI workshop on deep learning for artificial intelligence 32, 2016
162016
Method of estimating blur kernel from edge profiles in a blurry image
LN Smith
US Patent 8,594,447, 2013
112013
Best practices for applying deep learning to novel applications
LN Smith
arXiv preprint arXiv:1704.01568, 2017
102017
Estimating an image’s blur kernel from edge intensity profiles
L Smith
Naval research laboratory, 2012
92012
Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm
LN Smith, CC Olson, KP Judd, JM Nichols
Applied Optics 51 (17), 3941-3949, 2012
92012
Building one-shot semi-supervised (BOSS) learning up to fully supervised performance
LN Smith, A Conovaloff
Frontiers in Artificial Intelligence 5, 880729, 2022
82022
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Articles 1–20